import torch
from torch import nn

from einops import rearrange, repeat
from einops.layers.torch import Rearrange


# helpers
def pair(t):
    return t if isinstance(t, tuple) else (t, t)


# classes
class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn

    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)


class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout=0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        return self.net(x)


class Attention(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        # inner_dim: 1024
        inner_dim = dim_head * heads
        # 返回True or False
        project_out = not (heads == 1 and dim_head == dim)

        # 16
        self.heads = heads
        # scale = 0.125
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim=-1)
        # dropout=0.1
        self.dropout = nn.Dropout(dropout)
        # 输出：1024 * 3
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)

        self.to_out = nn.Sequential(
            # Linear(1024, 1024)
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        # (None, l(seq), d(emb))x(d(emb), d(head) * h) = (None, l(seq), d(head)*h)
        # Reshape(None, l(seq), d(head)*h)->(None, l(seq), h, d(head))
        qkv = self.to_qkv(x).chunk(3, dim=-1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), qkv)

        # (None, h, l(seq), d(head))
        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)
        attn = self.dropout(attn)
        # attention(q, k, v) = softmax(QK(t)/开根号d)V
        # dim(out) = (None, l(seq), h, d(head))
        out = torch.matmul(attn, v)
        # Reshape(out) -> (None, l(seq), d(emb))
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)


class Transformer(nn.Module):
    # Transformer(1024, 6, 16, 64, 2048, 0.1)
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                # (1024, Attention(1024, 16, 64, 0.1))
                PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),
                # (1024, FeedForward(1024, 2048, 0.1))
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x


class ViTBody(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim=1024, depth=6, heads=16, mlp_dim=2048, pool='cls', channels=3,
                 dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()
        # 计算块数N=HW/(P*P)
        # 例如块大小计算N=(256*256)/(32*32)=64
        image_height, image_width = pair(image_size)
        patch_height, patch_width = pair(patch_size)

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
        # num_patches=64 块
        num_patches = (image_height // patch_height) * (image_width // patch_width)
        # 每块的:3*32*32
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        self.to_patch_embedding = nn.Sequential(
            # 操作张量维度：batch_size * 3 * (245, 32) * (256 * 32) -> batch_size * (256, 256) * (32, 32, 3)
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),
            # 3072 -> 1024
            nn.Linear(patch_dim, dim),
        )

        # 位置编码
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        # test取0.1
        self.dropout = nn.Dropout(emb_dropout)

        # Transformer(1024, 6, 16, 64, 2048, 0.1)
        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.pool = pool

        # ----------------------#
        # 主要使用场景：
        # 不区分参数的占位符标识运算符
        #
        # if 某个操作 else Identity()
        # 在增减网络过程中，可以使得整个网络层数据不变，便于迁移权重数据
        # ----------------------#
        self.to_latent = nn.Identity()

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        x = self.to_patch_embedding(img)
        b, n, _ = x.shape

        # [1, 1, 1024] -> [64, 1, 1024]
        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=b)

        # 按照列拼接
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]

        x = self.to_latent(x)
        return self.mlp_head(x)


if __name__ == '__main__':
    model = ViTBody(
                image_size=256,
                patch_size=32,
                num_classes=20,
                dropout=0.1,
                emb_dropout=0.1)
    print(model)